Overview

Dataset statistics

Number of variables23
Number of observations4061
Missing cells11544
Missing cells (%)12.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory729.8 KiB
Average record size in memory184.0 B

Variable types

Categorical7
Numeric11
Boolean5

Alerts

Age is highly overall correlated with Creatinine_ClearanceHigh correlation
Year_DM_Diagnosed is highly overall correlated with DM_DurationHigh correlation
DM_Duration is highly overall correlated with Year_DM_DiagnosedHigh correlation
Waist is highly overall correlated with BMIHigh correlation
Fasting_Blood_Glucose_Value_SI_Units is highly overall correlated with HbA1C_Admission_ValueHigh correlation
HbA1C_Admission_Value is highly overall correlated with Fasting_Blood_Glucose_Value_SI_UnitsHigh correlation
BMI is highly overall correlated with WaistHigh correlation
Creatinine_Clearance is highly overall correlated with AgeHigh correlation
Cardiac_Arrest_Admission is highly imbalanced (90.2%)Imbalance
Non_Cardiac_Condition is highly imbalanced (81.5%)Imbalance
DM_Type is highly imbalanced (90.7%)Imbalance
Year_DM_Diagnosed has 2057 (50.7%) missing valuesMissing
DM_Duration has 1975 (48.6%) missing valuesMissing
DM_Type has 1872 (46.1%) missing valuesMissing
DM_Treatment has 1873 (46.1%) missing valuesMissing
Waist has 92 (2.3%) missing valuesMissing
Fasting_Blood_Glucose_Value_SI_Units has 1289 (31.7%) missing valuesMissing
HbA1C_Admission_Value has 1499 (36.9%) missing valuesMissing
Cholesterol_Value_SI_Units has 334 (8.2%) missing valuesMissing
Triglycerides_Value_SI_Units has 374 (9.2%) missing valuesMissing
BMI has 60 (1.5%) missing valuesMissing
Creatinine_Clearance has 69 (1.7%) missing valuesMissing
Year_DM_Diagnosed is highly skewed (γ1 = -30.88489352)Skewed
DM_Duration is highly skewed (γ1 = 31.49782961)Skewed
Cholesterol_Value_SI_Units is highly skewed (γ1 = 44.88015296)Skewed
Triglycerides_Value_SI_Units is highly skewed (γ1 = 22.38220136)Skewed

Reproduction

Analysis started2023-03-07 22:20:02.901859
Analysis finished2023-03-07 22:20:37.118888
Duration34.22 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
Male
2697 
Female
1364 

Length

Max length6
Median length4
Mean length4.6717557
Min length4

Characters and Unicode

Total characters18972
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 2697
66.4%
Female 1364
33.6%

Length

2023-03-07T22:20:37.203761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:20:37.380704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
male 2697
66.4%
female 1364
33.6%

Most occurring characters

ValueCountFrequency (%)
e 5425
28.6%
a 4061
21.4%
l 4061
21.4%
M 2697
14.2%
F 1364
 
7.2%
m 1364
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14911
78.6%
Uppercase Letter 4061
 
21.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5425
36.4%
a 4061
27.2%
l 4061
27.2%
m 1364
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
M 2697
66.4%
F 1364
33.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 18972
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5425
28.6%
a 4061
21.4%
l 4061
21.4%
M 2697
14.2%
F 1364
 
7.2%
m 1364
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5425
28.6%
a 4061
21.4%
l 4061
21.4%
M 2697
14.2%
F 1364
 
7.2%
m 1364
 
7.2%

Age
Real number (ℝ)

Distinct79
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.351884
Minimum18
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:20:37.579135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile40
Q152
median60
Q369
95-th percentile81
Maximum112
Range94
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.727595
Coefficient of variation (CV)0.21088977
Kurtosis-0.12256521
Mean60.351884
Median Absolute Deviation (MAD)9
Skewness0.0038224183
Sum245089
Variance161.99166
MonotonicityNot monotonic
2023-03-07T22:20:37.763831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 149
 
3.7%
72 138
 
3.4%
60 132
 
3.3%
57 132
 
3.3%
67 121
 
3.0%
59 119
 
2.9%
52 117
 
2.9%
56 114
 
2.8%
64 112
 
2.8%
70 111
 
2.7%
Other values (69) 2816
69.3%
ValueCountFrequency (%)
18 1
 
< 0.1%
23 1
 
< 0.1%
24 3
0.1%
25 6
0.1%
26 5
0.1%
27 4
0.1%
28 5
0.1%
29 5
0.1%
30 5
0.1%
31 7
0.2%
ValueCountFrequency (%)
112 1
 
< 0.1%
102 1
 
< 0.1%
99 5
0.1%
97 2
 
< 0.1%
96 1
 
< 0.1%
95 3
0.1%
94 6
0.1%
93 6
0.1%
92 6
0.1%
91 3
0.1%

Education
Categorical

Distinct6
Distinct (%)0.1%
Missing4
Missing (%)0.1%
Memory size31.9 KiB
No school
1965 
Below secondary school (high school)
969 
Secondary school (high school)
645 
Graduated from college
249 
Some college or vocational school
 
164

Length

Max length36
Median length33
Mean length20.731822
Min length9

Characters and Unicode

Total characters84109
Distinct characters28
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduated from college
2nd rowGraduated from college
3rd rowBelow secondary school (high school)
4th rowSecondary school (high school)
5th rowBelow secondary school (high school)

Common Values

ValueCountFrequency (%)
No school 1965
48.4%
Below secondary school (high school) 969
23.9%
Secondary school (high school) 645
 
15.9%
Graduated from college 249
 
6.1%
Some college or vocational school 164
 
4.0%
Post-graduate degree 65
 
1.6%
(Missing) 4
 
0.1%

Length

2023-03-07T22:20:37.920432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:20:38.110067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
school 5357
41.0%
no 1965
 
15.1%
secondary 1614
 
12.4%
high 1614
 
12.4%
below 969
 
7.4%
college 413
 
3.2%
graduated 249
 
1.9%
from 249
 
1.9%
some 164
 
1.3%
or 164
 
1.3%
Other values (3) 294
 
2.3%

Most occurring characters

ValueCountFrequency (%)
o 16645
19.8%
8995
10.7%
h 8585
10.2%
c 7548
9.0%
l 7316
8.7%
s 6391
 
7.6%
e 4082
 
4.9%
a 2570
 
3.1%
r 2406
 
2.9%
d 2242
 
2.7%
Other values (18) 17329
20.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67764
80.6%
Space Separator 8995
 
10.7%
Uppercase Letter 4057
 
4.8%
Close Punctuation 1614
 
1.9%
Open Punctuation 1614
 
1.9%
Dash Punctuation 65
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 16645
24.6%
h 8585
12.7%
c 7548
11.1%
l 7316
10.8%
s 6391
 
9.4%
e 4082
 
6.0%
a 2570
 
3.8%
r 2406
 
3.6%
d 2242
 
3.3%
g 2157
 
3.2%
Other values (9) 7822
11.5%
Uppercase Letter
ValueCountFrequency (%)
N 1965
48.4%
B 969
23.9%
S 809
19.9%
G 249
 
6.1%
P 65
 
1.6%
Space Separator
ValueCountFrequency (%)
8995
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1614
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1614
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71821
85.4%
Common 12288
 
14.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 16645
23.2%
h 8585
12.0%
c 7548
10.5%
l 7316
10.2%
s 6391
 
8.9%
e 4082
 
5.7%
a 2570
 
3.6%
r 2406
 
3.3%
d 2242
 
3.1%
g 2157
 
3.0%
Other values (14) 11879
16.5%
Common
ValueCountFrequency (%)
8995
73.2%
) 1614
 
13.1%
( 1614
 
13.1%
- 65
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 16645
19.8%
8995
10.7%
h 8585
10.2%
c 7548
9.0%
l 7316
8.7%
s 6391
 
7.6%
e 4082
 
4.9%
a 2570
 
3.1%
r 2406
 
2.9%
d 2242
 
2.7%
Other values (18) 17329
20.6%

Work
Categorical

Distinct3
Distinct (%)0.1%
Missing4
Missing (%)0.1%
Memory size31.9 KiB
No
2923 
Yes, Full-time
994 
Yes, Part-time
 
140

Length

Max length14
Median length2
Mean length5.3542026
Min length2

Characters and Unicode

Total characters21722
Distinct characters17
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes, Full-time
2nd rowYes, Full-time
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 2923
72.0%
Yes, Full-time 994
 
24.5%
Yes, Part-time 140
 
3.4%
(Missing) 4
 
0.1%

Length

2023-03-07T22:20:38.259934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:20:38.420622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
no 2923
56.3%
yes 1134
 
21.8%
full-time 994
 
19.1%
part-time 140
 
2.7%

Most occurring characters

ValueCountFrequency (%)
N 2923
13.5%
o 2923
13.5%
e 2268
10.4%
l 1988
9.2%
t 1274
 
5.9%
i 1134
 
5.2%
Y 1134
 
5.2%
s 1134
 
5.2%
, 1134
 
5.2%
1134
 
5.2%
Other values (7) 4676
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13129
60.4%
Uppercase Letter 5191
 
23.9%
Other Punctuation 1134
 
5.2%
Space Separator 1134
 
5.2%
Dash Punctuation 1134
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2923
22.3%
e 2268
17.3%
l 1988
15.1%
t 1274
9.7%
i 1134
 
8.6%
s 1134
 
8.6%
m 1134
 
8.6%
u 994
 
7.6%
a 140
 
1.1%
r 140
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
N 2923
56.3%
Y 1134
 
21.8%
F 994
 
19.1%
P 140
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 1134
100.0%
Space Separator
ValueCountFrequency (%)
1134
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18320
84.3%
Common 3402
 
15.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2923
16.0%
o 2923
16.0%
e 2268
12.4%
l 1988
10.9%
t 1274
7.0%
i 1134
 
6.2%
Y 1134
 
6.2%
s 1134
 
6.2%
m 1134
 
6.2%
u 994
 
5.4%
Other values (4) 1414
7.7%
Common
ValueCountFrequency (%)
, 1134
33.3%
1134
33.3%
- 1134
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21722
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2923
13.5%
o 2923
13.5%
e 2268
10.4%
l 1988
9.2%
t 1274
 
5.9%
i 1134
 
5.2%
Y 1134
 
5.2%
s 1134
 
5.2%
, 1134
 
5.2%
1134
 
5.2%
Other values (7) 4676
21.5%
Distinct3
Distinct (%)0.1%
Missing4
Missing (%)0.1%
Memory size31.9 KiB
No
3974 
Yes, after other symptoms
 
73
Yes, first presentation without prior symptoms
 
10

Length

Max length46
Median length2
Mean length2.5223071
Min length2

Characters and Unicode

Total characters10233
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 3974
97.9%
Yes, after other symptoms 73
 
1.8%
Yes, first presentation without prior symptoms 10
 
0.2%
(Missing) 4
 
0.1%

Length

2023-03-07T22:20:38.596955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:20:38.720247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
no 3974
91.9%
yes 83
 
1.9%
symptoms 83
 
1.9%
after 73
 
1.7%
other 73
 
1.7%
first 10
 
0.2%
presentation 10
 
0.2%
without 10
 
0.2%
prior 10
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 4160
40.7%
N 3974
38.8%
t 279
 
2.7%
s 269
 
2.6%
269
 
2.6%
e 249
 
2.4%
r 186
 
1.8%
m 166
 
1.6%
p 103
 
1.0%
, 83
 
0.8%
Other values (9) 495
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5824
56.9%
Uppercase Letter 4057
39.6%
Space Separator 269
 
2.6%
Other Punctuation 83
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4160
71.4%
t 279
 
4.8%
s 269
 
4.6%
e 249
 
4.3%
r 186
 
3.2%
m 166
 
2.9%
p 103
 
1.8%
a 83
 
1.4%
f 83
 
1.4%
h 83
 
1.4%
Other values (5) 163
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
N 3974
98.0%
Y 83
 
2.0%
Space Separator
ValueCountFrequency (%)
269
100.0%
Other Punctuation
ValueCountFrequency (%)
, 83
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9881
96.6%
Common 352
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4160
42.1%
N 3974
40.2%
t 279
 
2.8%
s 269
 
2.7%
e 249
 
2.5%
r 186
 
1.9%
m 166
 
1.7%
p 103
 
1.0%
a 83
 
0.8%
f 83
 
0.8%
Other values (7) 329
 
3.3%
Common
ValueCountFrequency (%)
269
76.4%
, 83
 
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 4160
40.7%
N 3974
38.8%
t 279
 
2.7%
s 269
 
2.6%
269
 
2.6%
e 249
 
2.4%
r 186
 
1.8%
m 166
 
1.6%
p 103
 
1.0%
, 83
 
0.8%
Other values (9) 495
 
4.8%
Distinct2
Distinct (%)< 0.1%
Missing4
Missing (%)0.1%
Memory size8.1 KiB
False
3943 
True
 
114
(Missing)
 
4
ValueCountFrequency (%)
False 3943
97.1%
True 114
 
2.8%
(Missing) 4
 
0.1%
2023-03-07T22:20:38.878318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing5
Missing (%)0.1%
Memory size8.1 KiB
True
2624 
False
1432 
(Missing)
 
5
ValueCountFrequency (%)
True 2624
64.6%
False 1432
35.3%
(Missing) 5
 
0.1%
2023-03-07T22:20:39.008538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing5
Missing (%)0.1%
Memory size8.1 KiB
True
2290 
False
1766 
(Missing)
 
5
ValueCountFrequency (%)
True 2290
56.4%
False 1766
43.5%
(Missing) 5
 
0.1%
2023-03-07T22:20:39.168906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

DM
Boolean

Distinct2
Distinct (%)< 0.1%
Missing5
Missing (%)0.1%
Memory size8.1 KiB
True
2173 
False
1883 
(Missing)
 
5
ValueCountFrequency (%)
True 2173
53.5%
False 1883
46.4%
(Missing) 5
 
0.1%
2023-03-07T22:20:39.326123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Year_DM_Diagnosed
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct46
Distinct (%)2.3%
Missing2057
Missing (%)50.7%
Infinite0
Infinite (%)0.0%
Mean1998.4251
Minimum10
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:20:39.480074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile1984.15
Q11996
median2002
Q32007
95-th percentile2011
Maximum2022
Range2012
Interquartile range (IQR)11

Descriptive statistics

Standard deviation63.20694
Coefficient of variation (CV)0.031628375
Kurtosis968.11143
Mean1998.4251
Median Absolute Deviation (MAD)5
Skewness-30.884894
Sum4004844
Variance3995.1172
MonotonicityNot monotonic
2023-03-07T22:20:39.657835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
2002 283
 
7.0%
2007 162
 
4.0%
1992 150
 
3.7%
1997 149
 
3.7%
2000 114
 
2.8%
2008 106
 
2.6%
2006 97
 
2.4%
2011 76
 
1.9%
2010 74
 
1.8%
2009 68
 
1.7%
Other values (36) 725
 
17.9%
(Missing) 2057
50.7%
ValueCountFrequency (%)
10 1
 
< 0.1%
20 1
 
< 0.1%
1967 1
 
< 0.1%
1970 1
 
< 0.1%
1972 2
 
< 0.1%
1973 2
 
< 0.1%
1974 1
 
< 0.1%
1975 1
 
< 0.1%
1977 7
0.2%
1978 1
 
< 0.1%
ValueCountFrequency (%)
2022 1
 
< 0.1%
2013 1
 
< 0.1%
2012 52
 
1.3%
2011 76
1.9%
2010 74
1.8%
2009 68
1.7%
2008 106
2.6%
2007 162
4.0%
2006 97
2.4%
2005 65
1.6%

DM_Duration
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct45
Distinct (%)2.2%
Missing1975
Missing (%)48.6%
Infinite0
Infinite (%)0.0%
Mean13.706616
Minimum-10
Maximum2004
Zeros30
Zeros (%)0.7%
Negative1
Negative (%)< 0.1%
Memory size31.9 KiB
2023-03-07T22:20:39.822102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile1
Q15.25
median10
Q316
95-th percentile27.75
Maximum2004
Range2014
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation61.986548
Coefficient of variation (CV)4.5223817
Kurtosis1007.2521
Mean13.706616
Median Absolute Deviation (MAD)5
Skewness31.49783
Sum28592
Variance3842.3321
MonotonicityNot monotonic
2023-03-07T22:20:40.035970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
10 339
 
8.3%
20 198
 
4.9%
15 170
 
4.2%
5 169
 
4.2%
12 113
 
2.8%
6 100
 
2.5%
4 85
 
2.1%
2 83
 
2.0%
7 82
 
2.0%
8 79
 
1.9%
Other values (35) 668
 
16.4%
(Missing) 1975
48.6%
ValueCountFrequency (%)
-10 1
 
< 0.1%
0 30
 
0.7%
1 78
1.9%
2 83
2.0%
3 76
1.9%
4 85
2.1%
5 169
4.2%
6 100
2.5%
7 82
2.0%
8 79
1.9%
ValueCountFrequency (%)
2004 1
 
< 0.1%
1992 1
 
< 0.1%
45 1
 
< 0.1%
42 1
 
< 0.1%
40 5
0.1%
38 1
 
< 0.1%
37 2
 
< 0.1%
36 1
 
< 0.1%
35 7
0.2%
34 1
 
< 0.1%

DM_Type
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing1872
Missing (%)46.1%
Memory size31.9 KiB
Type 2
2163 
Type 1
 
26

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters13134
Distinct characters7
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowType 2
2nd rowType 2
3rd rowType 2
4th rowType 2
5th rowType 2

Common Values

ValueCountFrequency (%)
Type 2 2163
53.3%
Type 1 26
 
0.6%
(Missing) 1872
46.1%

Length

2023-03-07T22:20:40.243273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:20:40.429228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
type 2189
50.0%
2 2163
49.4%
1 26
 
0.6%

Most occurring characters

ValueCountFrequency (%)
T 2189
16.7%
y 2189
16.7%
p 2189
16.7%
e 2189
16.7%
2189
16.7%
2 2163
16.5%
1 26
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6567
50.0%
Uppercase Letter 2189
 
16.7%
Space Separator 2189
 
16.7%
Decimal Number 2189
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 2189
33.3%
p 2189
33.3%
e 2189
33.3%
Decimal Number
ValueCountFrequency (%)
2 2163
98.8%
1 26
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
T 2189
100.0%
Space Separator
ValueCountFrequency (%)
2189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8756
66.7%
Common 4378
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 2189
25.0%
y 2189
25.0%
p 2189
25.0%
e 2189
25.0%
Common
ValueCountFrequency (%)
2189
50.0%
2 2163
49.4%
1 26
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 2189
16.7%
y 2189
16.7%
p 2189
16.7%
e 2189
16.7%
2189
16.7%
2 2163
16.5%
1 26
 
0.2%

DM_Treatment
Categorical

Distinct9
Distinct (%)0.4%
Missing1873
Missing (%)46.1%
Memory size31.9 KiB
Oral Hypoglycemic drugs
896 
Insulin
489 
Diet, Oral Hypoglycemic drugs
338 
Oral Hypoglycemic drugs, Insulin
164 
Diet
103 
Other values (4)
198 

Length

Max length40
Median length33
Mean length20.138026
Min length4

Characters and Unicode

Total characters44062
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDiet, Oral Hypoglycemic drugs
2nd rowInsulin
3rd rowOral Hypoglycemic drugs, Insulin
4th rowOral Hypoglycemic drugs
5th rowDiet, Oral Hypoglycemic drugs, Insulin

Common Values

ValueCountFrequency (%)
Oral Hypoglycemic drugs 896
22.1%
Insulin 489
 
12.0%
Diet, Oral Hypoglycemic drugs 338
 
8.3%
Oral Hypoglycemic drugs, Insulin 164
 
4.0%
Diet 103
 
2.5%
Diet, Insulin 100
 
2.5%
Diet, Oral Hypoglycemic drugs, Insulin 63
 
1.6%
None 34
 
0.8%
Diet, None 1
 
< 0.1%
(Missing) 1873
46.1%

Length

2023-03-07T22:20:40.553459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:20:40.778908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
oral 1461
25.0%
hypoglycemic 1461
25.0%
drugs 1461
25.0%
insulin 816
14.0%
diet 605
10.4%
none 35
 
0.6%

Most occurring characters

ValueCountFrequency (%)
4380
 
9.9%
l 3738
 
8.5%
y 2922
 
6.6%
g 2922
 
6.6%
c 2922
 
6.6%
r 2922
 
6.6%
i 2882
 
6.5%
s 2277
 
5.2%
u 2277
 
5.2%
e 2101
 
4.8%
Other values (13) 14719
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34575
78.5%
Space Separator 4380
 
9.9%
Uppercase Letter 4378
 
9.9%
Other Punctuation 729
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 3738
10.8%
y 2922
 
8.5%
g 2922
 
8.5%
c 2922
 
8.5%
r 2922
 
8.5%
i 2882
 
8.3%
s 2277
 
6.6%
u 2277
 
6.6%
e 2101
 
6.1%
n 1667
 
4.8%
Other values (6) 7945
23.0%
Uppercase Letter
ValueCountFrequency (%)
O 1461
33.4%
H 1461
33.4%
I 816
18.6%
D 605
13.8%
N 35
 
0.8%
Space Separator
ValueCountFrequency (%)
4380
100.0%
Other Punctuation
ValueCountFrequency (%)
, 729
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38953
88.4%
Common 5109
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 3738
 
9.6%
y 2922
 
7.5%
g 2922
 
7.5%
c 2922
 
7.5%
r 2922
 
7.5%
i 2882
 
7.4%
s 2277
 
5.8%
u 2277
 
5.8%
e 2101
 
5.4%
n 1667
 
4.3%
Other values (11) 12323
31.6%
Common
ValueCountFrequency (%)
4380
85.7%
, 729
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4380
 
9.9%
l 3738
 
8.5%
y 2922
 
6.6%
g 2922
 
6.6%
c 2922
 
6.6%
r 2922
 
6.6%
i 2882
 
6.5%
s 2277
 
5.2%
u 2277
 
5.2%
e 2101
 
4.8%
Other values (13) 14719
33.4%

Smoking_History
Categorical

Distinct4
Distinct (%)0.1%
Missing6
Missing (%)0.1%
Memory size31.9 KiB
Never Smoked
2464 
Current smoker (currently smoking or stopped smoking < 1 month)
982 
Past smoker (smoker stopped > 1year ago)
524 
Recent smoker (stopped smoking > 1 month and < 1 year)
 
85

Length

Max length63
Median length12
Mean length28.870284
Min length12

Characters and Unicode

Total characters117069
Distinct characters30
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCurrent smoker (currently smoking or stopped smoking < 1 month)
2nd rowNever Smoked
3rd rowNever Smoked
4th rowNever Smoked
5th rowNever Smoked

Common Values

ValueCountFrequency (%)
Never Smoked 2464
60.7%
Current smoker (currently smoking or stopped smoking < 1 month) 982
 
24.2%
Past smoker (smoker stopped > 1year ago) 524
 
12.9%
Recent smoker (stopped smoking > 1 month and < 1 year) 85
 
2.1%
(Missing) 6
 
0.1%

Length

2023-03-07T22:20:41.086951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T22:20:41.246565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
never 2464
12.7%
smoked 2464
12.7%
smoker 2115
10.9%
smoking 2049
10.6%
1676
8.7%
stopped 1591
8.2%
1 1152
6.0%
month 1067
 
5.5%
currently 982
 
5.1%
or 982
 
5.1%
Other values (7) 2809
14.5%

Most occurring characters

ValueCountFrequency (%)
15381
13.1%
e 13841
11.8%
o 10792
 
9.2%
r 10098
 
8.6%
m 7695
 
6.6%
k 6628
 
5.7%
s 6279
 
5.4%
n 5250
 
4.5%
t 5231
 
4.5%
d 4140
 
3.5%
Other values (20) 31734
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88635
75.7%
Space Separator 15381
 
13.1%
Uppercase Letter 6519
 
5.6%
Decimal Number 1676
 
1.4%
Math Symbol 1676
 
1.4%
Close Punctuation 1591
 
1.4%
Open Punctuation 1591
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13841
15.6%
o 10792
12.2%
r 10098
11.4%
m 7695
8.7%
k 6628
7.5%
s 6279
7.1%
n 5250
 
5.9%
t 5231
 
5.9%
d 4140
 
4.7%
p 3182
 
3.6%
Other values (9) 15499
17.5%
Uppercase Letter
ValueCountFrequency (%)
N 2464
37.8%
S 2464
37.8%
C 982
 
15.1%
P 524
 
8.0%
R 85
 
1.3%
Math Symbol
ValueCountFrequency (%)
< 1067
63.7%
> 609
36.3%
Space Separator
ValueCountFrequency (%)
15381
100.0%
Decimal Number
ValueCountFrequency (%)
1 1676
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1591
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 95154
81.3%
Common 21915
 
18.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13841
14.5%
o 10792
11.3%
r 10098
10.6%
m 7695
 
8.1%
k 6628
 
7.0%
s 6279
 
6.6%
n 5250
 
5.5%
t 5231
 
5.5%
d 4140
 
4.4%
p 3182
 
3.3%
Other values (14) 22018
23.1%
Common
ValueCountFrequency (%)
15381
70.2%
1 1676
 
7.6%
) 1591
 
7.3%
( 1591
 
7.3%
< 1067
 
4.9%
> 609
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15381
13.1%
e 13841
11.8%
o 10792
 
9.2%
r 10098
 
8.6%
m 7695
 
6.6%
k 6628
 
5.7%
s 6279
 
5.4%
n 5250
 
4.5%
t 5231
 
4.5%
d 4140
 
3.5%
Other values (20) 31734
27.1%

Heart_Rate
Real number (ℝ)

Distinct140
Distinct (%)3.5%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean85.175832
Minimum0
Maximum222
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:20:41.449941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57
Q171
median82
Q396
95-th percentile120
Maximum222
Range222
Interquartile range (IQR)25

Descriptive statistics

Standard deviation21.087922
Coefficient of variation (CV)0.24758105
Kurtosis3.2637604
Mean85.175832
Median Absolute Deviation (MAD)12
Skewness1.0140859
Sum345388
Variance444.70045
MonotonicityNot monotonic
2023-03-07T22:20:41.622476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 298
 
7.3%
90 203
 
5.0%
70 202
 
5.0%
100 151
 
3.7%
78 136
 
3.3%
88 126
 
3.1%
75 118
 
2.9%
72 105
 
2.6%
85 101
 
2.5%
76 88
 
2.2%
Other values (130) 2527
62.2%
ValueCountFrequency (%)
0 5
0.1%
8 1
 
< 0.1%
25 2
 
< 0.1%
27 1
 
< 0.1%
30 3
 
0.1%
34 2
 
< 0.1%
35 3
 
0.1%
36 2
 
< 0.1%
38 5
0.1%
40 11
0.3%
ValueCountFrequency (%)
222 1
 
< 0.1%
220 1
 
< 0.1%
200 2
< 0.1%
187 1
 
< 0.1%
180 4
0.1%
175 1
 
< 0.1%
174 1
 
< 0.1%
172 2
< 0.1%
170 3
0.1%
168 3
0.1%

Waist
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct120
Distinct (%)3.0%
Missing92
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean97.592341
Minimum25
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:20:41.782085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile72
Q188
median98
Q3109
95-th percentile125
Maximum194
Range169
Interquartile range (IQR)21

Descriptive statistics

Standard deviation17.450379
Coefficient of variation (CV)0.1788089
Kurtosis2.3121858
Mean97.592341
Median Absolute Deviation (MAD)10
Skewness-0.35062131
Sum387344
Variance304.51573
MonotonicityNot monotonic
2023-03-07T22:20:41.961729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 225
 
5.5%
100 171
 
4.2%
85 158
 
3.9%
110 151
 
3.7%
98 145
 
3.6%
105 132
 
3.3%
80 127
 
3.1%
95 123
 
3.0%
88 123
 
3.0%
102 111
 
2.7%
Other values (110) 2503
61.6%
ValueCountFrequency (%)
25 1
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
30 2
 
< 0.1%
32 9
0.2%
33 4
0.1%
34 5
0.1%
35 1
 
< 0.1%
36 8
0.2%
37 1
 
< 0.1%
ValueCountFrequency (%)
194 1
 
< 0.1%
191 2
 
< 0.1%
165 1
 
< 0.1%
160 1
 
< 0.1%
155 1
 
< 0.1%
152 1
 
< 0.1%
150 4
0.1%
148 5
0.1%
147 1
 
< 0.1%
146 1
 
< 0.1%

Fasting_Blood_Glucose_Value_SI_Units
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct458
Distinct (%)16.5%
Missing1289
Missing (%)31.7%
Infinite0
Infinite (%)0.0%
Mean8.3931782
Minimum0.33
Maximum235
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:20:42.180479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile4.4
Q15.5
median6.8
Q39.5
95-th percentile15.6
Maximum235
Range234.67
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.1325037
Coefficient of variation (CV)1.0880865
Kurtosis329.30432
Mean8.3931782
Median Absolute Deviation (MAD)1.6
Skewness15.946832
Sum23265.89
Variance83.402625
MonotonicityNot monotonic
2023-03-07T22:20:42.355531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 79
 
1.9%
5.2 76
 
1.9%
5.6 68
 
1.7%
5 68
 
1.7%
4.8 55
 
1.4%
5.4 53
 
1.3%
6.2 53
 
1.3%
5.8 53
 
1.3%
5.5 52
 
1.3%
5.7 49
 
1.2%
Other values (448) 2166
53.3%
(Missing) 1289
31.7%
ValueCountFrequency (%)
0.33 1
< 0.1%
0.38 1
< 0.1%
0.46 1
< 0.1%
0.47 1
< 0.1%
0.58 1
< 0.1%
0.61 1
< 0.1%
0.71 1
< 0.1%
0.81 1
< 0.1%
0.98 1
< 0.1%
1.01 1
< 0.1%
ValueCountFrequency (%)
235 1
< 0.1%
230 1
< 0.1%
156 1
< 0.1%
141 1
< 0.1%
135 1
< 0.1%
107 1
< 0.1%
100 1
< 0.1%
99 1
< 0.1%
87 1
< 0.1%
81 1
< 0.1%

HbA1C_Admission_Value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct384
Distinct (%)15.0%
Missing1499
Missing (%)36.9%
Infinite0
Infinite (%)0.0%
Mean7.5797931
Minimum0
Maximum88.4
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:20:42.533338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.9
Q15.9
median7
Q38.9225
95-th percentile11.9095
Maximum88.4
Range88.4
Interquartile range (IQR)3.0225

Descriptive statistics

Standard deviation2.7954479
Coefficient of variation (CV)0.36880267
Kurtosis272.19291
Mean7.5797931
Median Absolute Deviation (MAD)1.37
Skewness9.9580976
Sum19419.43
Variance7.814529
MonotonicityNot monotonic
2023-03-07T22:20:42.732740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 144
 
3.5%
5.9 71
 
1.7%
5.7 69
 
1.7%
5.8 65
 
1.6%
5.3 53
 
1.3%
5.6 52
 
1.3%
5.5 52
 
1.3%
7 49
 
1.2%
5 48
 
1.2%
5.4 48
 
1.2%
Other values (374) 1911
47.1%
(Missing) 1499
36.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
3.2 1
 
< 0.1%
3.3 1
 
< 0.1%
3.5 1
 
< 0.1%
3.6 1
 
< 0.1%
3.75 1
 
< 0.1%
3.9 12
0.3%
4 23
0.6%
4.01 1
 
< 0.1%
4.1 4
 
0.1%
ValueCountFrequency (%)
88.4 1
< 0.1%
20 1
< 0.1%
17.5 1
< 0.1%
16.6 1
< 0.1%
16 2
< 0.1%
15.8 1
< 0.1%
15.7 2
< 0.1%
15.4 1
< 0.1%
15.3 1
< 0.1%
15.2 2
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing7
Missing (%)0.2%
Memory size8.1 KiB
True
3455 
False
599 
(Missing)
 
7
ValueCountFrequency (%)
True 3455
85.1%
False 599
 
14.8%
(Missing) 7
 
0.2%
2023-03-07T22:20:42.893710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Cholesterol_Value_SI_Units
Real number (ℝ)

MISSING  SKEWED 

Distinct555
Distinct (%)14.9%
Missing334
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean4.9357773
Minimum0.07
Maximum489
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:20:43.037637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile2.83
Q13.8
median4.6
Q35.505
95-th percentile7
Maximum489
Range488.93
Interquartile range (IQR)1.705

Descriptive statistics

Standard deviation9.4548638
Coefficient of variation (CV)1.9155775
Kurtosis2127.6917
Mean4.9357773
Median Absolute Deviation (MAD)0.88
Skewness44.880153
Sum18395.642
Variance89.394449
MonotonicityNot monotonic
2023-03-07T22:20:43.231769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3 82
 
2.0%
4.5 80
 
2.0%
5.2 74
 
1.8%
4.6 73
 
1.8%
4.2 73
 
1.8%
5.6 70
 
1.7%
4.1 67
 
1.6%
4.9 67
 
1.6%
4 66
 
1.6%
4.8 65
 
1.6%
Other values (545) 3010
74.1%
(Missing) 334
 
8.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.08 3
0.1%
0.1 2
< 0.1%
0.13 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 2
< 0.1%
0.18 1
 
< 0.1%
0.7 1
 
< 0.1%
1 1
 
< 0.1%
1.2 1
 
< 0.1%
ValueCountFrequency (%)
489 1
< 0.1%
308 1
< 0.1%
15.7 1
< 0.1%
13.74 1
< 0.1%
11.8 2
< 0.1%
11.36 1
< 0.1%
11.29 1
< 0.1%
10.97 1
< 0.1%
10.12 1
< 0.1%
10 1
< 0.1%

Triglycerides_Value_SI_Units
Real number (ℝ)

MISSING  SKEWED 

Distinct432
Distinct (%)11.7%
Missing374
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean1.6739626
Minimum0.01
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:20:43.400903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.59
Q10.96
median1.4
Q32
95-th percentile3.6
Maximum71
Range70.99
Interquartile range (IQR)1.04

Descriptive statistics

Standard deviation1.6272688
Coefficient of variation (CV)0.97210585
Kurtosis901.04309
Mean1.6739626
Median Absolute Deviation (MAD)0.5
Skewness22.382201
Sum6171.9
Variance2.6480038
MonotonicityNot monotonic
2023-03-07T22:20:43.606233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 108
 
2.7%
1.1 107
 
2.6%
1.2 107
 
2.6%
1.3 102
 
2.5%
0.8 99
 
2.4%
0.9 98
 
2.4%
1.5 97
 
2.4%
1.4 91
 
2.2%
1.6 91
 
2.2%
1.8 80
 
2.0%
Other values (422) 2707
66.7%
(Missing) 374
 
9.2%
ValueCountFrequency (%)
0.01 6
0.1%
0.02 3
0.1%
0.03 1
 
< 0.1%
0.04 1
 
< 0.1%
0.1 1
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
0.18 1
 
< 0.1%
0.2 1
 
< 0.1%
0.25 1
 
< 0.1%
ValueCountFrequency (%)
71 1
< 0.1%
17.6 1
< 0.1%
17.4 1
< 0.1%
14.55 1
< 0.1%
11.56 1
< 0.1%
11.01 1
< 0.1%
10.59 1
< 0.1%
9.72 1
< 0.1%
9.14 1
< 0.1%
9 1
< 0.1%

BMI
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1187
Distinct (%)29.7%
Missing60
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean29.011505
Minimum13.34
Maximum342.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:20:43.791711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13.34
5-th percentile21.48
Q124.91
median27.76
Q331.59
95-th percentile39.54
Maximum342.78
Range329.44
Interquartile range (IQR)6.68

Descriptive statistics

Standard deviation8.9093348
Coefficient of variation (CV)0.30709661
Kurtosis507.7302
Mean29.011505
Median Absolute Deviation (MAD)3.3
Skewness16.91749
Sum116075.03
Variance79.376246
MonotonicityNot monotonic
2023-03-07T22:20:44.109868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.71 40
 
1.0%
27.68 38
 
0.9%
24.22 36
 
0.9%
29.41 30
 
0.7%
29.76 28
 
0.7%
31.25 27
 
0.7%
27.34 27
 
0.7%
25.95 26
 
0.6%
31.22 25
 
0.6%
28.73 23
 
0.6%
Other values (1177) 3701
91.1%
(Missing) 60
 
1.5%
ValueCountFrequency (%)
13.34 1
< 0.1%
14.84 1
< 0.1%
15.78 1
< 0.1%
16.42 2
< 0.1%
16.44 1
< 0.1%
16.77 1
< 0.1%
16.82 2
< 0.1%
16.9 1
< 0.1%
17.04 1
< 0.1%
17.07 1
< 0.1%
ValueCountFrequency (%)
342.78 1
< 0.1%
233.4 1
< 0.1%
221.14 1
< 0.1%
96.88 1
< 0.1%
69.25 1
< 0.1%
66.33 1
< 0.1%
65.38 1
< 0.1%
65.02 1
< 0.1%
63.57 1
< 0.1%
61.33 1
< 0.1%

Creatinine_Clearance
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3433
Distinct (%)86.0%
Missing69
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean88.192402
Minimum0.29
Maximum1164.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2023-03-07T22:20:44.316498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.29
5-th percentile24.502
Q156.8725
median82.84
Q3110.98
95-th percentile167.2385
Maximum1164.7
Range1164.41
Interquartile range (IQR)54.1075

Descriptive statistics

Standard deviation53.345335
Coefficient of variation (CV)0.6048745
Kurtosis100.44634
Mean88.192402
Median Absolute Deviation (MAD)26.99
Skewness6.0828736
Sum352064.07
Variance2845.7248
MonotonicityNot monotonic
2023-03-07T22:20:44.519995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.1 6
 
0.1%
83.2 6
 
0.1%
76.16 4
 
0.1%
97.17 4
 
0.1%
67.65 4
 
0.1%
76.26 4
 
0.1%
110.7 4
 
0.1%
79.9 4
 
0.1%
115.96 3
 
0.1%
98.4 3
 
0.1%
Other values (3423) 3950
97.3%
(Missing) 69
 
1.7%
ValueCountFrequency (%)
0.29 1
< 0.1%
0.52 1
< 0.1%
0.55 1
< 0.1%
0.58 1
< 0.1%
0.67 1
< 0.1%
0.78 1
< 0.1%
0.81 1
< 0.1%
0.98 1
< 0.1%
0.99 1
< 0.1%
1 1
< 0.1%
ValueCountFrequency (%)
1164.7 1
< 0.1%
1153.62 1
< 0.1%
875.43 1
< 0.1%
684.96 1
< 0.1%
626.18 1
< 0.1%
500.2 1
< 0.1%
460.63 1
< 0.1%
363.73 1
< 0.1%
321.8 1
< 0.1%
298.68 1
< 0.1%

Interactions

2023-03-07T22:20:32.867109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:08.991328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:10.698744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:12.670679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:14.990615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:17.142147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:19.544223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:21.907092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:24.511436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:27.293392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:30.125392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:33.110263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:09.110903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:10.826536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:12.841824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:15.118151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:17.281915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:19.770691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:22.328465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:24.760667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:27.556218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:30.389466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:33.347934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:09.324600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:10.986189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:13.069860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:15.312068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:17.520377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:19.969505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:22.625185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:24.990551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:27.810047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:30.689537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:33.606178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:09.471858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-03-07T22:20:15.451385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:17.901981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:20.231370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:22.839131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:25.238897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:28.076105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:30.971951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:33.834111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:09.611087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:11.307228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:13.628114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:15.603177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:18.072786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:20.408639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:23.047317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:25.450850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:28.306469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:31.187758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:34.090028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:09.759406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:11.556288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:13.801399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:15.810704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:18.253353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:20.664594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:23.257900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:25.693215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:28.683813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:31.390092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:34.323540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:09.890141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:11.718062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:13.948343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:15.978371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:18.389942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:20.835812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:23.450003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:25.973175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:28.889652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:31.579500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:34.570687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:10.066370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:11.847737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:14.219431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:16.236821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:18.528141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:21.031959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:23.632534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:26.237722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:29.106789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:31.789922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:34.743923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:10.257201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:12.079953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:14.419750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:16.573425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:18.690662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:21.266284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:23.802834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:26.510690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:29.335040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:32.021806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:34.915976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:10.427975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:12.328295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:14.637683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:16.737018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:19.076091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:21.482036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:24.038028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:26.737027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:29.551883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:32.259830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:35.126189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:10.557582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:12.497174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:14.827365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:16.900354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:19.269521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:21.686210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:24.308801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:26.966931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:29.817420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-07T22:20:32.508495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-07T22:20:44.737592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
AgeYear_DM_DiagnosedDM_DurationHeart_RateWaistFasting_Blood_Glucose_Value_SI_UnitsHbA1C_Admission_ValueCholesterol_Value_SI_UnitsTriglycerides_Value_SI_UnitsBMICreatinine_ClearanceGenderEducationWorkCardiac_Arrest_AdmissionNon_Cardiac_ConditionHypertensionDyslipidemiaDMDM_TypeDM_TreatmentSmoking_HistoryLipid_24_Collected
Age1.000-0.2950.3010.040-0.0980.0470.029-0.176-0.226-0.154-0.6380.1620.2250.4000.0620.0810.2890.1690.1960.2580.0260.1860.081
Year_DM_Diagnosed-0.2951.000-0.995-0.062-0.0400.027-0.0890.1950.092-0.1180.2160.0000.0250.1100.0000.0000.0000.0000.0780.0000.0000.0510.000
DM_Duration0.301-0.9951.0000.0510.044-0.0100.075-0.179-0.0720.108-0.2180.0000.0000.0000.0000.0000.0000.0220.0780.0000.0000.0000.000
Heart_Rate0.040-0.0620.0511.000-0.0090.0930.1120.014-0.0040.047-0.0620.1170.0200.0470.1760.1580.0910.0000.1020.0870.0000.0390.043
Waist-0.098-0.0400.044-0.0091.0000.1090.105-0.0050.1290.5090.2600.1210.0580.0550.0280.0000.1070.1180.1160.0260.0660.0650.067
Fasting_Blood_Glucose_Value_SI_Units0.0470.027-0.0100.0930.1091.0000.6350.0240.1500.113-0.0230.0400.0000.0000.0000.0570.0000.0220.0540.0000.0000.0000.000
HbA1C_Admission_Value0.029-0.0890.0750.1120.1050.6351.000-0.0050.1870.1430.0080.1040.0000.0270.0000.0000.1080.1430.4520.0000.0950.0370.000
Cholesterol_Value_SI_Units-0.1760.195-0.1790.014-0.0050.024-0.0051.0000.3870.0340.1470.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Triglycerides_Value_SI_Units-0.2260.092-0.072-0.0040.1290.1500.1870.3871.0000.1550.1610.0000.0240.0130.0000.0000.0250.0400.0180.0250.0980.0210.059
BMI-0.154-0.1180.1080.0470.5090.1130.1430.0340.1551.0000.3820.0700.0000.0000.0160.0000.0150.0000.0130.0000.0330.0090.000
Creatinine_Clearance-0.6380.216-0.218-0.0620.260-0.0230.0080.1470.1610.3821.0000.0350.1340.2160.0000.0630.1350.0670.0640.0620.0680.1400.000
Gender0.1620.0000.0000.1170.1210.0400.1040.0230.0000.0700.0351.0000.3530.3810.0000.0000.2070.1210.1620.0130.1520.4330.065
Education0.2250.0250.0000.0200.0580.0000.0000.0000.0240.0000.1340.3531.0000.3790.0000.0390.1600.0450.0740.0950.0470.1960.000
Work0.4000.1100.0000.0470.0550.0000.0270.0000.0130.0000.2160.3810.3791.0000.0000.0660.2410.1190.1670.0720.1140.2120.052
Cardiac_Arrest_Admission0.0620.0000.0000.1760.0280.0000.0000.0000.0000.0160.0000.0000.0000.0001.0000.0970.0000.0340.0000.0000.0300.0110.031
Non_Cardiac_Condition0.0810.0000.0000.1580.0000.0570.0000.0000.0000.0000.0630.0000.0390.0660.0971.0000.0530.0000.0370.0000.0000.0540.028
Hypertension0.2890.0000.0000.0910.1070.0000.1080.0000.0250.0150.1350.2070.1600.2410.0000.0531.0000.3940.3330.0040.1200.1860.000
Dyslipidemia0.1690.0000.0220.0000.1180.0220.1430.0000.0400.0000.0670.1210.0450.1190.0340.0000.3941.0000.3260.0000.1360.1150.000
DM0.1960.0780.0780.1020.1160.0540.4520.0000.0180.0130.0640.1620.0740.1670.0000.0370.3330.3261.0000.0000.0000.1190.017
DM_Type0.2580.0000.0000.0870.0260.0000.0000.0000.0250.0000.0620.0130.0950.0720.0000.0000.0040.0000.0001.0000.1350.0000.000
DM_Treatment0.0260.0000.0000.0000.0660.0000.0950.0000.0980.0330.0680.1520.0470.1140.0300.0000.1200.1360.0000.1351.0000.0490.082
Smoking_History0.1860.0510.0000.0390.0650.0000.0370.0000.0210.0090.1400.4330.1960.2120.0110.0540.1860.1150.1190.0000.0491.0000.000
Lipid_24_Collected0.0810.0000.0000.0430.0670.0000.0000.0000.0590.0000.0000.0650.0000.0520.0310.0280.0000.0000.0170.0000.0820.0001.000

Missing values

2023-03-07T22:20:35.498913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-07T22:20:36.144048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-07T22:20:36.719584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

GenderAgeEducationWorkCardiac_Arrest_AdmissionNon_Cardiac_ConditionHypertensionDyslipidemiaDMYear_DM_DiagnosedDM_DurationDM_TypeDM_TreatmentSmoking_HistoryHeart_RateWaistFasting_Blood_Glucose_Value_SI_UnitsHbA1C_Admission_ValueLipid_24_CollectedCholesterol_Value_SI_UnitsTriglycerides_Value_SI_UnitsBMICreatinine_Clearance
0Male62Graduated from collegeYes, Full-timeNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1Male52Graduated from collegeYes, Full-timeNoNoYesYesYesNaN10.0Type 2Diet, Oral Hypoglycemic drugsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2Female73Below secondary school (high school)NoNoNoYesYesNoNaNNaNNaNNaNCurrent smoker (currently smoking or stopped smoking < 1 month)75.060.07.00NaNYes5.381.8520.0024.12
3Male46NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4Female46NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5Female64Secondary school (high school)NoNoNoYesYesYes2002.010.0Type 2InsulinNever Smoked117.085.011.93NaNYes5.402.2433.2963.60
6Female56Below secondary school (high school)NoNoNoNoYesYes1988.024.0Type 2Oral Hypoglycemic drugs, InsulinNever Smoked82.085.04.407.1No4.701.3031.22114.24
7Female78Below secondary school (high school)NoNoNoYesNoNoNaNNaNNaNNaNNever Smoked90.090.06.205.6No4.111.1119.5627.55
8Male54No schoolNoNoNoYesNoNoNaNNaNNaNNaNNever Smoked75.091.04.005.6Yes5.202.2023.0592.37
9Female54No schoolNoNoNoNoNoNoNaNNaNNaNNaNNever Smoked90.093.0NaNNaNNoNaNNaN23.8173.42
GenderAgeEducationWorkCardiac_Arrest_AdmissionNon_Cardiac_ConditionHypertensionDyslipidemiaDMYear_DM_DiagnosedDM_DurationDM_TypeDM_TreatmentSmoking_HistoryHeart_RateWaistFasting_Blood_Glucose_Value_SI_UnitsHbA1C_Admission_ValueLipid_24_CollectedCholesterol_Value_SI_UnitsTriglycerides_Value_SI_UnitsBMICreatinine_Clearance
4051Male74Below secondary school (high school)NoNoNoYesYesYes1987.025.0Type 2Diet, InsulinNever Smoked88.0NaN10.88.3Yes5.141.1028.4156.50
4052Male76Secondary school (high school)Yes, Full-timeNoNoYesNoNoNaNNaNNaNNaNCurrent smoker (currently smoking or stopped smoking < 1 month)140.070.0NaNNaNNoNaNNaN25.7134.66
4053Male61Some college or vocational schoolYes, Part-timeNoYesYesYesYes1993.020.0Type 2Oral Hypoglycemic drugsNever Smoked87.090.04.27.4Yes5.102.5029.7650.65
4054Female56No schoolNoNoNoYesNoYes2004.08.0Type 2Diet, Oral Hypoglycemic drugsNever Smoked62.0115.07.28.1No7.442.3233.12109.82
4055Male41Below secondary school (high school)Yes, Full-timeNoNoYesYesYesNaN7.0Type 2Oral Hypoglycemic drugsPast smoker (smoker stopped > 1year ago)106.079.0NaNNaNYes5.901.9027.9788.77
4056Male75No schoolNoNoNoYesYesYes2001.011.0Type 2InsulinNever Smoked98.092.07.58.8Yes4.672.2223.5145.28
4057Male56Below secondary school (high school)NoYes, first presentation without prior symptomsNoNoYesYes1996.015.0Type 2Oral Hypoglycemic drugsCurrent smoker (currently smoking or stopped smoking < 1 month)120.093.07.911.6Yes6.661.5329.3983.77
4058Male55Secondary school (high school)NoNoNoNoNoYes2006.0NaNType 2Oral Hypoglycemic drugsCurrent smoker (currently smoking or stopped smoking < 1 month)109.072.09.211.6Yes3.950.8022.8489.61
4059Male77No schoolNoNoNoYesNoNoNaNNaNNaNNaNNever Smoked100.085.0NaNNaNYes6.710.6522.0431.00
4060Male73No schoolNoNoNoYesYesYes1987.025.0Type 2Oral Hypoglycemic drugs, InsulinCurrent smoker (currently smoking or stopped smoking < 1 month)100.0117.0NaNNaNYes4.101.7729.0167.36